10
comments:

Very nice job, Charles. It appears that Mann has not been as cooperative as would be hoped in a science setting. Perhaps he has good reason for what others describe as his not being forthcoming with data or answering questions, etc. Considering that the hockey stick has been viewed with such unassailability by the crafters of Kyoto, understanding the statistics behind this temperature analysis is important.

The tide seems to have shifted. I sense that the global warming ideologues are on the run. The MSM built them up---and the new media has brought them back down to earth. Radical leftists merely wish to damage the American economy. We are supposedly the Great Satan who must be punished for polluting the environment.

It’s bad enough that we cannot take for granted the integrity and objectivity of the liberal arts practitioners. Credentialled hard scientists now also whore themselves simply to satisfy the demands of the hate America crowd.

Congrats. Nice job. Looks like they limited your word count. It would have been interesting to see this with a more full explication of the methodologies employed.

David--

Science is a tool. Like any tool, if you abuse it it won't be available to you when you need it.

Indirectly, the chief problem, as always, is the MSM. If any of the thousands of reporters giving breath to global warming issues knew a damn thing about statistics or climatology, this thing would be a whole lot more clearly understood. You can't explain what you don't understand yourself. Much easier to just parrot the press release.

Don't go overboard, David. I know --- and know by transitivity --- some of the hockey team, NCAR is visible from my computer as I type. They're good people. Like all scientists, liberal arts professors, and those posting on this blog, they're people. Mann et al came up with a statistical scheme that they thought made sense at the time, and loked like it answered an exciting question. I've reviewed papers myself that had this same property --- after you get done conditioning your data you're accidentally made your hypothesis an axiom, even though every step sounds perfectly reasonable. Then, once you've published, and people have gotten invested in it, you defend it instead of being an ideal scientist who can drop a hypothesis without a qualm.

I think the underlying lesson is that one should maintain a reasonable skepticism about any science, and no matter how strong a hypothesis seems, still you have to remember that new evidence might come in.

Fresh, they didn't really limit my word count as much as I was trying to make the point and get out. I'm not all that strong in that area (Chuck?) so I didn't want to get too technical. Luckily, the information-theoretic argument I mentioned on my original post is sufficiently foundational that you can get a lot of traction without linear algebra.